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In this work, we propose to use anthropometrics and physiological data to estimate cardiorespiratory fitness (CRF) in free-living and analyze the relation between estimated CRF and running performance. In particular, we use the ratio between running speed and heart rate (HR) as predictor for CRF estimation in free-living. The ratio is representative of fitness as lower HR at a given speed is expected...
Identifying the feature of road line accurately is one of the most important things helps the smart driving assistant system operate effectively. So, we propose a new method to estimate the lane curvature. The method bases on the information from radar of stationary objects and target vehicle head in order to calculate the lane curvature parameter. At first, we introduce the lane curvature parameter...
Concerns for the environment, health and safety are of major importance and have been attracting considerable attention around the globe due to the new environmental challenges that are threatening our planet. In this paper, we propose to enhance the fault detection of an air quality monitoring network (AQMN) by using wavelet principal component analysis (WPCA)-based on generalized likelihood ratio...
This paper proposes a novel methodology for the calculation and estimation of transmission line impedance with the use of real time data from synchrophasor measurement units. Current synchrophasor standards do not provide impedance data and must therefore be calculated. The algorithms used in this paper include impedance calculation, outlier detection and elimination as well as denoising using regularized...
The trade-off between performance and power consumptionfor dynamic and complex applications is inevitable andis considered as a key design challenge for system architects. Performance statistics for hard real time systems must be readilyavailable for a feedback system, specifically for reconfigurablearchitectures. The feedback in terms of performance statisticscan be useful for run-time reconfiguration...
A new image-based visual servoing method based on point features is presented to partially decouple the position and orientation controls. The control laws are designed for rotation and translation, respectively. Rotation takes priority over translation, but the movement of features caused by rotation is compensated in the translation control law. A monitor is designed to manage the adjustments for...
One of the major issues in smart grid monitoring, protection and power quality event identification is accurate phasor estimation. Proper phasor estimation is more important especially during fault, because the signals doesn’t have pure sine wave anymore and include different harmonic components and Decaying DC offset, so Proper phasor estimation in smart grids are very important issue. This paper...
Cyber-attack technologies have been evolved continuously. As a result, new attacks and their variants appearevery day. Also, intelligent and malicious attackers use varioustechniques to bypass the current signature and anomalydetection based intrusion detection systems. To detect thenew attacks more effectively, new anomaly detection modelis needed. In this paper, we propose a novel anomaly detectionmethod...
Environmental sensors monitor supercomputing facility health, generating massive data in the largest facilities. Current state-of-the-art is for human operators to evaluate environmental data by hand. This approach will not be viable on Exascale machines, nor is it ideal on current systems. We evaluate effectiveness of the DBSCAN algorithm for identifying anomalies in supercomputing sensor data. We...
In this paper, we propose a new characteristic measure relative people density and motion dynamics for the purpose of long-term crowd monitoring. While many related works focus on direct people counting and absolute density estimation, we will show that relative densities provide reliable information on crowd behaviour. Furthermore, we will discuss the derivation of a so-called Congestion Level of...
In this paper, we present TR-BREATH, a time-reversal (TR) based, contact-free, accurate breathing monitoring system capable of multi-person breathing rate estimation within a short period of time (e.g., around one minute) using off-the-shelf WiFi devices. TR-BREATH exploits the fine-grained channel state information (CSI) on WiFi devices to capture the minor variations caused by breathing. To amplify...
A new efficient measure for predicting estimation accuracy is proposed and successfully applied to multistream-based unsupervised adaptation of ASR systems to address data uncertainty when the ground-truth is unknown. The proposed measure is an extension of the M-measure, which predicts confidence in the output of a probability estimator by measuring the divergences of probability estimates spaced...
Human age estimation is an important research topic and can find its applications in such as commodity recommendation and security monitoring. The establishment of existing estimators basically follows a same pipeline, i.e., an estimator is built from a given training dataset like FG-NET and then evaluated on a holdout testing set to determine its effectiveness. In doing so, a usually-followed assumption...
Physiological monitoring is prone to artifacts originating from various sources such as motion, device malfunction, and interference. The artifact occurrence not only elevates false alarm rates in clinics but also complicates data analysis in research. When techniques to characterize signal dynamics and the underlying physiology are applied (e.g., heart rate variability), noise and artifacts can produce...
This paper describes a low-power metabolic equivalents (METs) estimation method for monitoring physical activity. Long-term continuous METs monitoring can contribute to detection of non-communicable diseases. The proposed system consists of dedicated METs estimation hardware and a non-volatile CPU. A test is fabricated in a 130-nm CMOS with a ferroelectric capacitor process. Evaluation results show...
To reduce the chance of traffic crashes, many driver monitoring systems (DMSs) have been developed. A DMS warns the driver under abnormal driving conditions. However, traditional approaches require enumerating abnormal driving conditions. In this paper, we propose a novel DMS, which models the driver's normal driving statuses based on sparse reconstruction. The proposed DMS compares the driver's statuses...
Accurate localization of randomly deployed sensor nodes is critically important in wireless sensor networks (WSNs) deployed for monitoring and tracking applications. The localization challenge has been posed as a multidimensional global optimization problem in earlier literature. Many swarm intelligence algorithms have been proposed for accurate localization. The untapped vast potential of the artificial...
In this paper, we study the optimum sensing of a time-varying random event with a sensor powered by energy harvesting devices. The system aims at reconstructing a band-unlimited continuous-time random process by using discrete-time samples collected by a sensor. Due to the random nature of the harvested energy, the sensor might not have sufficient energy to perform a sensing operation at a desired...
An adequate drug dosage is often difficult to be achieved in personalized medicine. Especially for critical application, such as anesthesia. To reach the right dose adjustment, electrochemical biosensors can be used to measure the actual concentration of the drug in the patient's blood. Normally, since many drugs have to be measured simultaneously, Cyclic Voltammetry (CV) is the most convenient electrochemical...
In this paper, we aim to develop an efficient speculation framework for a heterogeneous cluster. Speculation is a common mechanism that identifies ‘slow’ node in a cluster and starts redundant tasks on other nodes to guarantee the reliability. We consider MapReduce/Hadoop as a representative computing platform, and our general goal is to accurately and quickly identify the straggler nodes during the...
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